medical_records_processing · healthcare · workflow
Advent Health Partners uses active learning and automation to speed up medical record labeling with Labelbox
Extracting data from millions of paper medical records to train ML models was slow and expensive because records regularly exceed 500 or even thousands of pages and arrive from numerous providers, hospitals, and insurers with no consistent formatting in mixed formats such as PDFs, faxes, and scans.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Medical records ingestion
Paper medical records arrive from multiple providers, hospitals, and insurers in inconsistent formats including PDFs, faxes, and scans.
Tools used
LabelboxOCRNLPmodel-assisted labeling
Outcome
By leveraging Labelbox's active learning and model-assisted labeling, AHP dramatically sped up medical record labeling, reducing average time per label from 13 seconds to 8 seconds and cutting 25 hours off each specific labeling task.
Results
Time saved13 seconds
Volume8 seconds
Running sinceNovember 2022
Grounding & classification
Source type: vendor customer story
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